Identification of potentially cancerous lesions in CT and MRI studies is a common task of radiologists. An important aspect of this task is determining the extent and the volume such a lesion occupies. This is a tedious task to accomplish manually, due to the irregular shape of suspected lesions and the ever-increasing number of slices captured by diagnostic imaging systems.
A method of automating the process is to have a computer perform such a task once the lesion has been identified. This task is commonly referred to in the image-processing domain as image or volume segmentation and techniques referred to as region growing are typically applied. Region growing algorithms typically use local image characteristics, such as image intensity variations to decide whether a neighboring voxel (3D volume images) or pixel (2D planar images) is to be added to the growing region. However, segmenting lesions from normal anatomy is a difficult task, (see Hong Shen, et. al., “A New Algorithm for Local Surface Smoothing with Application to Chest Wall Nodule Segmentation in Lung CT Data,” Medical Imaging 2004, Proceedings of SPIE Vol. 5370) as the image differences the lesion and normal anatomy often are not discernable in terms of voxel intensity values, e.g., Hounsfield units HU. As a consequence, region-growing tasks often expand beyond the target and, in the case of segmenting lesions, include regions that are most likely to be healthy tissue or normal anatomy.
The subsequent phase is to ascertain a surface that separates the lesion from normal anatomy within the initial segmented region. Common approaches to the problem are to fit a plane or low degree polynomial surface, or a sequence of dividing lines on a slice-by-slice basis, to demark the boundary between normal anatomy and the suspected lesion. Unfortunately, these methods are too restrictive and often fail to produce desirable results. A primary reason is that such approaches are overly simplistic and do not account for the local geometry, or the variability of surfaces within a volume. The selection of a surface model imposes an implicit assumption on the kind of local surface one expects.
The present invention approaches this problem by using methods that overcome these limitations. The invention is an ensemble of methods, where each is increasingly sophisticated, but requires more computational resources. All of these methods use the boundary of the leading edge of an expanding segmentation front that is likely to be part of the normal anatomy and finds a surface that holds this boundary fixed.